Productivity monitoring software varies widely in what it captures. At the lighter, more common end, most software collects:
Some tools go further with more invasive monitoring features like keystroke logging, screen recording, or message content. That said, teams that need or want these features are relatively uncommon.
If you're evaluating a tool, the real question isn't just what it collects. It's how much is too much, and where does this one fit?
The same questions apply if you are an employee who has just learned your employer uses such a tool.
For most rollouts, teams aren’t trying to surveil their members. Instead, they’re trying to stop losing hours guessing why work is (or isn’t) getting done.
There isn't a standard set of data that every productivity monitoring tool collects. Tools can range anywhere from doing what punch clocks did to watching everything you can see on a screen.
That said, most productivity monitoring tools can track the following data.
The most basic layer, and the thing almost every tool does, is keeping track of when somebody worked.
Time and attendance metrics entail essentially zero controversy because, at its core, time tracking is just a more effortless and more precise version of punch clocks. Here’s what time and attendance data typically looks like:
Virtually every productivity monitoring platform builds its tracking capabilities with time tracking as the foundation.
This type of data is significantly more detailed than time and attendance data. Tools with time and activity tracking sit very far still from the surveillance end of the spectrum, but it can understandably make teams nervous because many conversations about productivity percentages stem from this metric.
Here are a few common examples of activity data:
It's important to distinguish activity metrics from keystroke logging. Most productivity monitoring tools that report activity percentages only measure the presence of keyboard and mouse input. They don't record what employees type. Some monitoring tools do include keystroke logging, but that's a separate, more invasive capability.
Activity data is very contextual and should be treated as such. Someone on customer calls or in meetings will naturally have lower activity levels than someone doing data entry, even if both are equally productive.
For that reason, activity data shouldn't be used as a standalone measure of employee performance.
Whereas activity tracking focuses on how much movement was made by the employee, this category focuses on how employees spend their time. It shows which applications they use, which websites they visit during work hours, and how long they spend in each.
This category usually covers:
As AI adoption grows, many productivity monitoring tools now identify when and how employees use AI applications.
Some tools, including Hubstaff, go a step further by classifying apps and websites as productive or non-productive. Because productivity varies by role, these classifications are typically configurable rather than fixed.
Like any monitoring data, web and app tracking works best when it's configured thoughtfully. A website that's productive for one role may be irrelevant or distracting for another.
Screenshots are one of the more sensitive types of monitoring data. For some organizations, they're important for compliance, client billing, or verifying work. For others, they're unnecessary.
Because screenshots can capture personal or confidential information, most tools let administrators decide:
These controls matter because screenshots are best suited to work verification, not continuous surveillance.
For example, Hubstaff offers optional screenshots that administrators can configure based on their needs. Team members can blur or delete screenshots if they capture personal or confidential information.
This is where individual data points become operational insights.
Instead of zeroing in on one point in time or a single metric, the workforce analytics looks at big-picture patterns across weeks, teams, and even industries, to clearly understand how work is happening inside the organization.
This category includes workforce analytics metrics like:
These insights help organizations answer questions that individual activity data can't. Are some teams consistently overloaded? Is work distributed evenly? Are certain processes creating bottlenecks? Where is capacity available?
Instead of measuring individual output, workforce analytics supports better planning, staffing, resource allocation, and operational decision-making.
The honest answer depends on the tool. Below is what most invasive software can generally do, and what transparency-first tools deliberately avoid doing at all.
Some tools log every keystroke. Others, Hubstaff among them, do not. They measure keyboard and mouse movement, not what the user is typing. There's an important distinction between knowing someone's hands were active and knowing what they typed.
There are tools that can read the actual content. More restrained ones can’t, although email or message contents can be visible if a screenshot capture takes place when a team member is on that specific window. Hubstaff doesn’t monitor email or message content — it can track time spent on platforms like Slack and Gmail, but not the messaging content inside the tools.
Reputable tools don't. Keystroke-logging tools could, in theory, be one more reason teams should be extremely careful before implementing tools with that functionality.
Standard activity or app tracking has no access to file contents. Continuous screen recording is different as it can reveal whatever happens to be on screen at the time, which is why it is important for teams to have the ability to configure recording and screenshot settings.
Surveillance-heavy tools sometimes offer this. Transparency-first tools don't.
Automated tracking does not collect anything different from tracking that someone starts and stops themselves. It captures the same time, activity, app usage, and workforce analytics as manual tracking. The difference is that tracking starts automatically based on a policy instead of relying on employees to remember to start and stop a timer.
This approach is most common on company-owned or managed devices, where organizations want consistent, organization-wide tracking. Rather than depending on individual habits, every device follows the same rules, reducing gaps in timesheets and creating more reliable workforce data for reporting, payroll, compliance, and operational planning.
Because tracking happens automatically, transparency becomes even more important. Organizations should clearly explain what is being tracked, when tracking is active, and how the data will be used before rolling it out. Depending on local laws, employee notification or consent may also be required.
With information as powerful as productivity monitoring data, access should not be all-or-nothing. A reliable tool should allow organizations to assign visibility according to what a specific role needs, which naturally prevents people from tracking more than necessary.
Here are examples of how roles can be configured within productivity monitoring software:
The general philosophy behind this hierarchy can be adapted to most industries. Each role should see only as much as its real responsibilities require, and the size of that access should grow only when the responsibilities do too.
This approach to fair, reasonable access is a core guiding principle to how we design Hubstaff. Leaders and managers can see only data that their role requires, while team members can always access their own data.
There are advantages and disadvantages to employee monitoring. It can be a powerful tool if used right, but it can just as easily damage workplace trust and harm employee relationships if not.
Particularly, how you use productivity data yields the most palpable results for better or worse. Productivity data should be treated as additional context, not the determining factor in decisions related to managing people.
For instance, if a project fell behind, then the data must be used to better understand why it happened, so the process can be improved, instead of using the data to find a responsible person to blame.
The same activity numbers that look like evidence of a problem can just as easily be evidence of a bad deadline, or a task that was harder than anyone accounted for when the schedule was drawn up. The only way to tell the difference is to have conversations with the people who perform these jobs, which isn’t something any tool can do on your behalf.
Workload balancing shows this clearly too. Time data can tell you who's been putting in long hours and who hasn't, but it doesn't know the story behind those long hours. Maybe someone is overwhelmed, mismatched to the work, or distracted by something outside of work entirely — the data can't tell the difference. It just points at a pattern, and a person has to decide what the pattern means and what to do with it.
Coaching is where this gets the most personal, because anybody can use a number as a verdict on someone's performance. A dip in activity looks the same whether someone is coasting or just doing work that requires more thinking than clicking. The data can't tell you which.
Zoom out to workforce analytics, and nothing changes aside from the scale. You might see a trend here or a spike there, but none of this changes the fact that a person will have to read and act on it.
Everything here starts with communication.
Before you track anything, employees should know what's being collected, how, and why. You need to have a real, human conversation with everyone in the team, and you need to have an extensive written tracking policy. If you don’t, you will leave people to assume the worst, which they usually will.
Permissions matter here, too. Who can see the data should be specific (i.e., real, named roles, not "management"), and that access should map to what each role needs to be successful.
You also need to spell out what retention looks like:
A retention window signals that the data exists for a reason as opposed to indefinitely.
Compliance adds another layer for certain industries. Healthcare organizations often need HIPAA compliance, while enterprise buyers look for SOC 2 or GDPR certifications.
With that said, the use of a certified tool doesn’t make an organization inherently compliant. A tool being certified means it can meet compliance standards, but that would still depend on how the organization configures and uses the tool. Since requirements vary by region and industry, we strongly recommend reviewing the specific laws that apply before rolling anything out.
Organizations do not collect productivity data simply to monitor activity. Besides, having powerful productivity data at your fingertips would be wasteful if you only use it to watch people work.
The goal, instead, is to transform operational data into insights that improve workforce performance, productivity, and decision-making.
Productivity monitoring tools give you the means to collect that data, but that data’s real value appears when it’s scaled to an organizational level, where it can help leaders make decisions about resource planning, team capacity, and getting team performance where it needs to be to sustain long-term growth.